mmdet.models.necks.nas_fpn 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell
from mmcv.runner import BaseModule, ModuleList

from ..builder import NECKS

[文档]@NECKS.register_module() class NASFPN(BaseModule): """NAS-FPN. Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection <>`_ Args: in_channels (List[int]): Number of input channels per scale. out_channels (int): Number of output channels (used at each scale) num_outs (int): Number of output scales. stack_times (int): The number of times the pyramid architecture will be stacked. start_level (int): Index of the start input backbone level used to build the feature pyramid. Default: 0. end_level (int): Index of the end input backbone level (exclusive) to build the feature pyramid. Default: -1, which means the last level. add_extra_convs (bool): It decides whether to add conv layers on top of the original feature maps. Default to False. If True, its actual mode is specified by `extra_convs_on_inputs`. init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, in_channels, out_channels, num_outs, stack_times, start_level=0, end_level=-1, add_extra_convs=False, norm_cfg=None, init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')): super(NASFPN, self).__init__(init_cfg) assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels self.num_ins = len(in_channels) # num of input feature levels self.num_outs = num_outs # num of output feature levels self.stack_times = stack_times self.norm_cfg = norm_cfg if end_level == -1: self.backbone_end_level = self.num_ins assert num_outs >= self.num_ins - start_level else: # if end_level < inputs, no extra level is allowed self.backbone_end_level = end_level assert end_level <= len(in_channels) assert num_outs == end_level - start_level self.start_level = start_level self.end_level = end_level self.add_extra_convs = add_extra_convs # add lateral connections self.lateral_convs = nn.ModuleList() for i in range(self.start_level, self.backbone_end_level): l_conv = ConvModule( in_channels[i], out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) self.lateral_convs.append(l_conv) # add extra downsample layers (stride-2 pooling or conv) extra_levels = num_outs - self.backbone_end_level + self.start_level self.extra_downsamples = nn.ModuleList() for i in range(extra_levels): extra_conv = ConvModule( out_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) self.extra_downsamples.append( nn.Sequential(extra_conv, nn.MaxPool2d(2, 2))) # add NAS FPN connections self.fpn_stages = ModuleList() for _ in range(self.stack_times): stage = nn.ModuleDict() # gp(p6, p4) -> p4_1 stage['gp_64_4'] = GlobalPoolingCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # sum(p4_1, p4) -> p4_2 stage['sum_44_4'] = SumCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # sum(p4_2, p3) -> p3_out stage['sum_43_3'] = SumCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # sum(p3_out, p4_2) -> p4_out stage['sum_34_4'] = SumCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # sum(p5, gp(p4_out, p3_out)) -> p5_out stage['gp_43_5'] = GlobalPoolingCell(with_out_conv=False) stage['sum_55_5'] = SumCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # sum(p7, gp(p5_out, p4_2)) -> p7_out stage['gp_54_7'] = GlobalPoolingCell(with_out_conv=False) stage['sum_77_7'] = SumCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) # gp(p7_out, p5_out) -> p6_out stage['gp_75_6'] = GlobalPoolingCell( in_channels=out_channels, out_channels=out_channels, out_norm_cfg=norm_cfg) self.fpn_stages.append(stage)
[文档] def forward(self, inputs): """Forward function.""" # build P3-P5 feats = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) ] # build P6-P7 on top of P5 for downsample in self.extra_downsamples: feats.append(downsample(feats[-1])) p3, p4, p5, p6, p7 = feats for stage in self.fpn_stages: # gp(p6, p4) -> p4_1 p4_1 = stage['gp_64_4'](p6, p4, out_size=p4.shape[-2:]) # sum(p4_1, p4) -> p4_2 p4_2 = stage['sum_44_4'](p4_1, p4, out_size=p4.shape[-2:]) # sum(p4_2, p3) -> p3_out p3 = stage['sum_43_3'](p4_2, p3, out_size=p3.shape[-2:]) # sum(p3_out, p4_2) -> p4_out p4 = stage['sum_34_4'](p3, p4_2, out_size=p4.shape[-2:]) # sum(p5, gp(p4_out, p3_out)) -> p5_out p5_tmp = stage['gp_43_5'](p4, p3, out_size=p5.shape[-2:]) p5 = stage['sum_55_5'](p5, p5_tmp, out_size=p5.shape[-2:]) # sum(p7, gp(p5_out, p4_2)) -> p7_out p7_tmp = stage['gp_54_7'](p5, p4_2, out_size=p7.shape[-2:]) p7 = stage['sum_77_7'](p7, p7_tmp, out_size=p7.shape[-2:]) # gp(p7_out, p5_out) -> p6_out p6 = stage['gp_75_6'](p7, p5, out_size=p6.shape[-2:]) return p3, p4, p5, p6, p7
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